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Jun 14, 2017
06/17
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scikit-learn
software
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scikit-learn: machine learning in Python To restore the repository, download the bundle scikit-learn-scikit-learn_-_2017-06-14_07-21-15.bundle and run: git clone scikit-learn-scikit-learn_-_2017-06-14_07-21-15.bundle -b master Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2017-06-14
Topics: GitHub, code, software, git
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Oct 5, 2017
10/17
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scikit-learn
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scikit-learn: machine learning in Python To restore the repository download the bundle scikit-learn-scikit-learn_-_2017-10-05_17-21-33.bundle and run: git clone scikit-learn-scikit-learn_-_2017-10-05_17-21-33.bundle -b master Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2017-10-05
Topics: GitHub, code, software, git
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Apr 6, 2020
04/20
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scikit-learn
software
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scikit-learn: machine learning in Python To restore the repository download the bundle wget https://archive.org/download/github.com-scikit-learn-scikit-learn_-_2020-04-06_07-53-43/scikit-learn-scikit-learn_-_2020-04-06_07-53-43.bundle and run: git clone scikit-learn-scikit-learn_-_2020-04-06_07-53-43.bundle Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2020-04-06
Topics: GitHub, code, software, git
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Nov 26, 2019
11/19
by
scikit-learn
software
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scikit-learn: machine learning in Python To restore the repository download the bundle wget https://archive.org/download/github.com-scikit-learn-scikit-learn_-_2019-11-26_07-05-06/scikit-learn-scikit-learn_-_2019-11-26_07-05-06.bundle and run: git clone scikit-learn-scikit-learn_-_2019-11-26_07-05-06.bundle Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2019-11-26
Topics: GitHub, code, software, git
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0.0
Jan 23, 2021
01/21
by
scikit-learn
software
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scikit-learn: machine learning in Python To restore the repository download the bundle wget https://archive.org/download/github.com-scikit-learn-scikit-learn_-_2021-01-23_17-22-17/scikit-learn-scikit-learn_-_2021-01-23_17-22-17.bundle and run: git clone scikit-learn-scikit-learn_-_2021-01-23_17-22-17.bundle Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2021-01-23
Topics: GitHub, code, software, git
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0.0
Jan 24, 2021
01/21
by
scikit-learn
software
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scikit-learn: machine learning in Python To restore the repository download the bundle wget https://archive.org/download/github.com-scikit-learn-scikit-learn_-_2021-01-24_12-41-06/scikit-learn-scikit-learn_-_2021-01-24_12-41-06.bundle and run: git clone scikit-learn-scikit-learn_-_2021-01-24_12-41-06.bundle Source: https://github.com/scikit-learn/scikit-learn Uploader: scikit-learn Upload date: 2021-01-24
Topics: GitHub, code, software, git
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Perma.cc archive of https://github.com/scikit-learn/scikit-learn created on 2016-02-07 18:19:15+00:00.
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Perma.cc archive of https://github.com/scikit-learn/scikit-learn/commits/master?page=57 created on 2016-02-13 11:51:59+00:00.
A set of python modules for machine learning and data mining This item contains old versions of the Arch Linux package for python2-scikit-learn . Website of the upstream project: http://scikit-learn.sourceforge.net/ License: BSD See the Arch Linux Archive documentation for details.
Topics: archlinux, archlinux package, python2-scikit-learn
A set of python modules for machine learning and data mining This item contains old versions of the Arch Linux package for python-scikit-learn . Website of the upstream project: http://scikit-learn.sourceforge.net/ License: BSD See the Arch Linux Archive documentation for details.
Topics: archlinux, archlinux package, python-scikit-learn
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Valerio Maggio - Scikit-learn to "learn them all" [EuroPython 2014] [24 July 2014] Scikit-learn is a powerful library, providing implementations for many of the most popular machine learning algorithms. This talk will provide an overview of the "batteries" included in Scikit-learn, along with working code examples and internal insights, in order to get the best for our machine learning code. ----- **Machine Learning** is about *using the right features, to build the right...
Topics: machine learning, scikit-learn, scipy, numpy, matplotlib, EuroPython2014, Python
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**Machine Learning** is about *using the right features, to build the right models, to achieve the right tasks* [[Flach, 2012]][0] However, to come up with a definition of what actually means **right** for the problem at the hand, it is required to analyse huge amounts of data, and to evaluate the performance of different algorithms on these data. However, deriving a working machine learning solution for a given problem is far from being a *waterfall* process. It is an iterative process where...
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Florian Wilhelm - Extending Scikit-Learn with your own Regressor [EuroPython 2014] [25 July 2014] We show how to write your own robust linear estimator within the Scikit-Learn framework using as an example the Theil-Sen estimator known as "the most popular nonparametric technique for estimating a linear trend". ----- Scikit-Learn (http://scikit-learn.org/) is a well-known and popular framework for machine learning that is used by Data Scientists all over the world. We show in a...
Topics: machine learning, nonparametric methods, robust methods, scikit-learn, data sience, EuroPython2014,...
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Scikit-Learn (http://scikit-learn.org/) is a well-known and popular framework for machine learning that is used by Data Scientists all over the world. We show in a practical way how you can add your own estimator following the interfaces of Scikit-Learn. First we give a small introduction to the design of Scikit-Learn and its inner workings. Then we show how easily Scikit-Learn can be extended by creating an own estimator. In order to demonstrate this, we extend Scikit-Learn by the popular and...
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765
Sep 18, 2013
09/13
by
Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg; Jake Vanderplas; Alexandre Passos; David Cournapeau; Matthieu Brucher; Matthieu Perrot; Édouard Duchesnay
texts
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Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....
Source: http://arxiv.org/abs/1201.0490v2
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Jun 30, 2018
06/18
by
Alexandre Abraham; Fabian Pedregosa; Michael Eickenberg; Philippe Gervais; Andreas Muller; Jean Kossaifi; Alexandre Gramfort; Bertrand Thirion; Gäel Varoquaux
texts
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Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find...
Topics: Machine Learning, Computing Research Repository, Computer Vision and Pattern Recognition, Learning,...
Source: http://arxiv.org/abs/1412.3919
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Oct 24, 2014
10/14
by
Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gael
texts
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This article is from Frontiers in Neuroinformatics , volume 8 . Abstract Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden...
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930868
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Jul 29, 2017
07/17
by
Jeff Klukas
movies
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Jeff Klukas http://pyohio.org/schedule/presentation/284/ The Python data ecosystem provides amazing tools to quickly get up and running with machine learning models, but the path to stably serving them in production is not so clear. We'll discuss details of wrapping a minimal REST API around scikit-learn, training and persisting models in batch, and logging decisions, then compare to some other common approaches to productionizing models. PyOhio is a free (thanks sponsors!) annual conference...
Topics: pyohio, pyohio_2017, JeffKlukas
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3.0
Aug 11, 2020
08/20
by
Fabian Pedregosa; Gal Varoquaux; Alex; re Gramfort; Vincent Michel; Bertr; Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg; Jake V; erplas; Alex; re Passos; David Cournapeau; Matthieu Brucher; Matthieu Perrot; douard Duchesnay
texts
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Source: http://academictorrents.com/details/5ba4939a00a9b21629a0ad7d376898b768d997a3
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Perma.cc archive of https://medium.com/mlreview/topic-modeling-with-scikit-learn-e80d33668730 created on 2020-02-19 20:17:23+00:00.
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Oct 6, 2018
10/18
by
justmarkham
software
eye 41
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Jupyter notebooks from the scikit-learn video series Introduction to machine learning with scikit-learn This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was featured on Kaggle's blog in 2015. There are 9 video tutorials totaling 4 hours, each with a corresponding Jupyter notebook . The notebook contains everything you see in the video: code, output, images, and comments. Note: The notebooks in this repository have been...
Topics: GitHub, code, software, git
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14
Apr 10, 2020
04/20
by
justmarkham
software
eye 14
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:robot::zap: Daily scikit-learn tips 🤖⚡ Daily scikit-learn tips New tips are posted on LinkedIn , Twitter , and Facebook every weekday! 👉 Sign up to receive 5 tips by email every week 👈 List of all tips Click to view the Jupyter notebook for a tip, or click to discuss the tip on LinkedIn: # | Description | Links--- | --- | ---1 | Use ColumnTransformer to apply different preprocessing to different columns | 2 | Seven ways to select columns using ColumnTransformer | 3 | What is the...
Topics: GitHub, code, software, git
Episode Summary: Andreas Müller talks about how he fell in love with scikit-learn and his continuous work there as the package maintainer. We also cover his work at Amazon and why he left to work on open source; his recent book on machine learning in Python; sustainability and future of sklearn in the "deep learning world", and his impressions of ... Read More
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20
Apr 9, 2020
04/20
by
justmarkham
software
eye 20
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comment 0
:robot::zap: Daily scikit-learn tips 🤖⚡ Daily scikit-learn tips New tips are posted on LinkedIn , Twitter , and Facebook every weekday! 👉 Sign up to receive 5 tips by email every week 👈 List of all tips Click to view the Jupyter notebook for a tip, or click to discuss the tip on LinkedIn: # | Description | Links--- | --- | ---1 | Use ColumnTransformer to apply different preprocessing to different columns | 2 | Seven ways to select columns using ColumnTransformer | 3 | What is the...
Topics: GitHub, code, software, git
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Perma.cc archive of http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html created on 2017-04-29 16:40:57+00:00.
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Perma.cc archive of http://scikit-learn.org/stable/about.html#funding created on 2018-08-14 19:46:04+00:00.
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Perma.cc archive of http://scikit-learn.org/stable/modules/feature_extraction.html created on 2017-08-07 22:04:52+00:00.
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4.0
movies
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scikit-learn 0.21 was recently released and this presentation will give an overview its main new features in general and present the new implementation of Gradient Boosted Trees. Gradient Boosted Trees (also known as Gradient Boosting Machines) are very competitive supervised machine learning models especially on tabular data. Scikit-learn offered a traditional implementation of this family of methods for many years. However its computational performance was no longer competitive and was...
Topics: Cython, Data, Machine-Learning, Multi-Threading, Scientific Libraries (Numpy/Pandas/SciKit/...),...
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Perma.cc archive of https://scikit-learn.org/stable/ created on 2019-01-30 18:32:43+00:00.
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Perma.cc archive of http://scikit-learn.org/ created on 2017-07-03 14:37:57+00:00.
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7.0
movies
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Data privacy is having an ever-increasing impact on the way data is stored, processed, accessed and utilised, as the legal and ethical effects of data protection regulations take effect around the globe. Differential privacy, considered by many to be the strongest privacy guarantee currently available, gives robust, provable guarantees on protecting privacy, and allows tasks to be completed on data with guarantees on the privacy of individuals in that data. This naturally extends to machine...
Topics: Data Privacy, Data Protection, Machine-Learning, Open-Source, Scientific Libraries...
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3.0
movies
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Machine learning algorithms used in the classification of text are Support Vector Machines, k Nearest Neighbors but the most popular algorithm to implement is Naive Bayes because of its simplicity based on Bayes Theorem. The Naive Bayes classifier is able to memorise the relationships between the training attributes and the outcome and predicts by multiplying the conditional probabilities of the attributes with the assumption that they are independent of the outcome. It is popularly used in...
Topics: Scientific Libraries (Numpy/Pandas/SciKit/...), Data, Natural Language Processing, EuroPython2018,...
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80
May 20, 2017
05/17
by
scikit-garden
software
eye 80
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comment 0
Download the bundle scikit-garden-scikit-garden_-_2017-05-19_22-41-42.bundle and run: git clone scikit-garden-scikit-garden_-_2017-05-19_22-41-42.bundle -b master A garden for scikit-learn compatible trees Scikit-Garden Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests. Weights at different depths of a MondrianTree Ordered prediction intervals on the Boston dataset. Installation Scikit-Garden depends on NumPy, SciPy,...
Topics: GitHub, code, software, git
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12
Jun 29, 2018
06/18
by
Guillaume Lemaitre; Fernando Nogueira; Christos K. Aridas
texts
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Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1609.06570
2
2.0
Dec 14, 2019
12/19
by
OCDevel
audio
eye 2
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TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ocdevel.com/mlg/24 for notes and resources
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113
May 25, 2017
05/17
by
ujjwalkarn
software
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Download the bundle ujjwalkarn-DataSciencePython_-_2017-05-08_05-04-54.bundle and run: git clone ujjwalkarn-DataSciencePython_-_2017-05-08_05-04-54.bundle -b master common data analysis and machine learning tasks using python Python Data Science Tutorials This repo contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. Curated list of R tutorials for Data Science, NLP and Machine Learning . Comprehensive topic-wise list of Machine Learning and Deep Learning...
Topics: GitHub, code, software, git
The goal of this report is to perform an analysis of software tools that could be employed to perform basic research and development of Anomaly-Based Intrusion Detection Systems. The software tools reviewed include; Environment for Developing KDD-Applications Supported by Index-Structures (ELKI), RapidMiner, SHOGUN (toolbox) Waikato Environment for Knowledge Analysis (Weka) (machine learning), and Scikit-learn. From the analysis, it is recommended to employ the SHOGUN (toolbox) or Scikit-learn...
Topics: DTIC Archive, ARMY RESEARCH LAB ADELPHI MD COMPUTATIONAL AND INFORMATION SCIENCES DIRECTORATE,...
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28
Jun 29, 2018
06/18
by
Philippe Besse; Brendan Guillouet; Jean-Michel Loubes
texts
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Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments: R, Python Scikit-learn, Spark MLlib on three public use cases: character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient...
Topics: Learning, Applications, Computing Research Repository, Statistics
Source: http://arxiv.org/abs/1609.09619
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Holger Peters - Using Scikit-Learn's interface for turning Spaghetti Data Science into Maintainable Software [EuroPython 2015] [21 July 2015] [Bilbao, Euskadi, Spain] Finding a good structure for number-crunching code can be a problem, this especially applies to routines preceding the core algorithms: transformations such as data processing and cleanup, as well as feature construction. With such code, the programmer faces the problem, that their code easily turns into a sequence of highly...
Topics: python, data-science, machine-learning, cleancode, sklearn, Best Practice, Testing, EuroPython2015,...
"Let's learn about useful Machine Learning algorithms, invoking them from common libraries (pandas & scikit-learn). The session starts with a brief introduction to ML and the different approaches we can apply. After that we will practice exploring real datasets, cleaning and optimizing the data, and using ML algorithms to find hidden insights. NO need to know about maths, machine learning, nor python; this is an introductory but profitable workshop!"
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10.0
Dec 9, 2017
12/17
by
dnouri
software
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A scikit-learn compatible neural network library that wraps pytorch To restore the repository download the bundle wget https://archive.org/download/github.com-dnouri-skorch_-_2017-12-08_18-01-05/dnouri-skorch_-_2017-12-08_18-01-05.bundle and run: git clone dnouri-skorch_-_2017-12-08_18-01-05.bundle Source: https://github.com/dnouri/skorch Uploader: dnouri Upload date: 2017-12-08
Topics: GitHub, code, software, git
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Jan 7, 2020
01/20
by
amitness
software
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Curated list of libraries for a faster machine learning workflow toolbox Curated libraries for a faster workflow Phase: Data Data Annotation Image: makesense.ai Text: doccano , prodigy Datasets Text: nlp-datasets , curse-words , badwords , LDNOOBW , english-words (A text file containing over 466k English words) , 10K most common words Image: 1 million fake faces Dataset search engine: datasetlist , UCI Machine Learning Datasets Importing Data Audio: pydub Video: pytube (download youTube vidoes)...
Topics: GitHub, code, software, git
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5.0
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Python is the lingua franca for data analytics and machine learning. Its superior productivity makes it the preferred tool for prototyping. However, traditional Python packages are not necessarily designed to provide high performance and scalability for large datasets. From this talk you will learn how to get close-to-native performance with Intel-optimized packages, such as numpy, scipy, and scikit-learn. The next part of the talk is focused on getting high performance and scalability from...
Topics: Analytics, Big Data, Distributed Systems, Machine-Learning, Scientific Libraries...
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12
Dec 11, 2017
12/17
by
dnouri
software
eye 12
favorite 0
comment 0
A scikit-learn compatible neural network library that wraps pytorch To restore the repository download the bundle wget https://archive.org/download/github.com-dnouri-skorch_-_2017-12-11_11-46-21/dnouri-skorch_-_2017-12-11_11-46-21.bundle and run: git clone dnouri-skorch_-_2017-12-11_11-46-21.bundle Source: https://github.com/dnouri/skorch Uploader: dnouri Upload date: 2017-12-11
Topics: GitHub, code, software, git
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Philipp Mack - Python in the world of retail and mail order [EuroPython 2015] [24 July 2015] At Blue Yonder a lot of different python packages, provided by the community, as well as our own self-written packages, are used in order to provide flexible solutions to our problems. In this talk I'll present a walkthrough of a generic python application example for demand and purchase order quantity calculations, putting together those packages in an orderly way. The example will feature real world...
Topics: redis, data-science, pytest, pandas, automation, configuration, case study, deployment, scipy,...
4
4.0
Apr 25, 2019
04/19
by
CodeAnd_
audio
eye 4
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In Episode 10 we talk about what data science is and the fascinating information that comes from it. Want to get involved and start to tackle some big data? Jerome Scheuring tells us how. iTunes RSS SoundCloud Google Play Music Anywhere podcasts are available Show Notes Topics: Data Science (3:35) Picks (48:48) Picks: scikit-learn – (Jerome) Permaculture A Designers Manual – (Andrew) Daisy Disk– (Landon) Farmbot – (Landon) Social: Meetup Twitter
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133
Jun 19, 2017
06/17
by
donnemartin
software
eye 133
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Recently updated with 50 new notebooks! Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. data-science-ipython-notebooks Index deep-learning tensorflow theano keras caffe scikit-learn statistical-inference-scipy pandas matplotlib numpy python-data kaggle-and-business-analyses spark mapreduce-python amazon web services...
Topics: GitHub, code, software, git
